Probabilistic Graphical Model Based Complex Activity Recognition
Abstract：Complex activity typically consists of multiple agents or sub-activities which coordinate or interactive with each other over a period of time. Due to the complexity, variety and uncertainty, it is quite challenging for complex activity analysis and modeling. In this proposal, we aim to systematically analysis and recognize complex activity using probabilistic graphical models. Complex activity is divided into two categories: the composite activity and the interactive activity. Different parsing, representation and recognition methods will be studied for each category. We will discover and model the complex spatiotemporal relationships, design efficient model learning and inference algorithms. We also explore multiple granularity analysis to find a unified representation framework in order to simultaneously recognize both the complex activity and the sub-activities. In addition, we will employ the deep learning method to discover high-level nonlinear features instead of the traditional hand-craft features. The parameters of the deep neural network and the graphical models will be jointly optimized. We will finally develop practical complex activity recognition prototype systems for applications to intelligent wearable device, human machine interaction and sports analysis.
Keywords: activity recognition; activity understanding; complex activity; probabilistic graphical model
Contact:
ZHANG Yifan
E-mail: yfzhang@nlpr.ia.ac.cn